Automatic question answering (QA) is a form of information retrieval in which focused answers are generated for either user queries, e.g., a key word search, or ad hoc questions, e.g., questions in a natural language format. A question answering system can attempt to handle various question types including: fact, list, definition, how, why, hypothetical, semantically constrained, and cross-lingual questions. The questions can be either in a closed domain or open domain. Closed-domain questions are under a specific knowledge domain (e.g., medicine or physics). Open-domain questions can relate to any topics and usually rely on general knowledge for answers. Question answering is regarded as requiring more complex natural language processing (NLP) techniques than other types of information retrieval such as document retrieval.
Language modeling is another form of information retrieval. Language modeling refers to a task to identify the next word of a text sequence of words, given identities of existing words of the sequence. Language modeling help estimating the relative likelihood of different phrases based on the prediction of the next word. Language modeling tasks are useful in various natural language processing applications, such as speech recognition, speech tagging, parsing, machine translation, handwriting recognition, etc.